lark-agent

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add_task_comment

Add a plain-text comment to a work item to capture meeting notes, decisions, or handling records, leaving an auditable trail alongside field updates.

Instructions

为指定工作项添加一条评论（纯文本）。

适用场景:
- 需要在工作项下沉淀沟通结论、会议纪要、处理记录
- 希望 Agent 在更新字段之外留下“可审计的文字说明”

Args:
    issue_id: 工作项 ID，必填。
    content: 评论内容（纯文本），必填。内容为空会报错。
    project: 项目标识符（可选）。可以是项目名称或 project_key。
             不传则使用环境变量默认项目。
    work_item_type: 工作项类型名称（可选）。不传则使用默认类型。
    user_key: (可选) 飞书用户标识符 (X-USER-KEY)，用于以特定用户身份进行操作。

Returns:
    JSON 字符串。
    - success=true 时，data 至少包含 comment_id。
    - 失败时返回纯文本错误信息（由 with_error_handling 统一处理）。

Examples:
    add_task_comment(issue_id=123, content="已与研发确认：本周五前完成联调")

Input Schema

TableJSON Schema

Name	Required	Description	Default
`content`	Yes
`project`	No
`issue_id`	Yes
`user_key`	No
`work_item_type`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.0.2Sep 6, 2026

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral disclosure burden. It discloses that content must be plain text, empty content causes an error, optional parameters fall back to defaults, and return/error formats are JSON or plain text via with_error_handling. This is solid coverage for a create-comment operation, though it does not discuss permissions beyond the user_key identity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with an opening summary, bulleted use cases, an Args section, Returns section, and a concrete example. Each section earns its place and the important behavioral caveats are front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is complete for a tool with five parameters and an output schema. It covers parameter semantics, defaults, return shape (including comment_id on success), error handling, and provides a runnable example. There are no significant gaps that would prevent an agent from calling this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must fully compensate. It explains all five parameters: issue_id is required, content is required and must not be empty, project accepts a name or project_key and defaults to the environment variable, work_item_type defaults to the default type, and user_key identifies the acting Feishu user. This adds substantial meaning beyond the raw JSON schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear verb and resource: '为指定工作项添加一条评论（纯文本）' (add a plain-text comment to a specified work item). It distinguishes itself from sibling tools like list_task_comments, update_task_comment, and delete_task_comment by explicitly framing this as the creation action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes an '适用场景' section with concrete use cases: recording communication conclusions, meeting minutes, handling records, and leaving auditable text notes beyond field updates. It gives clear situational context, though it does not explicitly state when not to use this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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